Keffi-Backend / ml_models /bert_analyzer.py
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from typing import Dict, Any
class ClinicalAssessmentEngine:
"""
Ithu thaan namba main Local AI Engine. Neenga kudutha ella list-aiyum
(Depression types, Alleviation states, Attrition reasons, Patient Intent)
ithulla exact-a train panna porom.
"""
def __init__(self):
# Placeholder for loading actual BERT / LLM pipeline
# e.g., self.model = pipeline('text-classification', model='medical-bert')
# --- THE MASTER CLINICAL TAXONOMY (As strictly defined by you) ---
self.master_categories = {
"Main_Clinical_Types": [
"Major Depressive Disorder (MDD)", "Persistent Depressive Disorder (PDD / Dysthymia)",
"Bipolar Disorder (Manic Depression)", "Postpartum (Perinatal) Depression",
"Seasonal Affective Disorder (SAD)", "Psychotic Depression",
"Premenstrual Dysphoric Disorder (PMDD)", "Atypical Depression"
],
"Clinical_Sub_Types": [
"Disruptive Mood Dysregulation Disorder (DMDD)", "Treatment-Resistant Depression (TRD)",
"Substance/Medication-Induced Depression", "Depression Due to Another Medical Condition",
"Smiling Depression (High-Functioning)", "Melancholic Depression",
"Agitated Depression", "Double Depression", "Minor Depression"
],
"Rare_and_Specific_Origins": [
"Catatonic Depression", "Endogenous Depression", "Reactive (Exogenous) Depression",
"Existential Depression", "Masked Depression", "Geriatric Depression",
"Recurrent Brief Depression", "Unipolar Depression", "Vascular Depression"
],
"Niche_Types": [
"Cyclothymia", "Mixed Anxiety-Depressive Disorder", "Perimenopausal Depression",
"Post-Schizophrenic Depression", "Adjustment Disorder", "Antenatal (Prenatal) Depression",
"Prolonged Grief Disorder", "Male Depressive Syndrome", "Burnout-Induced Depression",
"Anergic Depression", "D-MER (Dysphoric Milk Ejection Reflex)", "Post-coital Dysphoria (PCD)"
],
"Treatment_Alleviation_States": [
"Partial Response", "Residual Symptoms", "Tachyphylaxis (Poop-Out)",
"Breakthrough Depression", "Treatment-Resistant (TRD)", "Full Remission",
"Sustained Recovery", "Spontaneous Remission", "Placebo Effect",
"Palliative Alleviation", "Nocebo Effect", "The Honeymoon Effect",
"Relapse", "Recurrence", "Pseudo-Resistance", "Emotional Blunting (Apathy)",
"Treatment-Emergent Affective Switch (TEAS)", "Discontinuation Syndrome (Withdrawal)"
],
"Attrition_and_Loss_Of_FollowUp": [
# Attrition Domains
"Corporate/HR Attrition", "Customer Churn", "Academic Attrition (Dropouts)",
"Therapeutic/Clinical Trial Attrition",
# Patient Intent Loss
"Voluntary Withdrawal", "Intentional Non-compliance", "Cured Perception", "Unintentional Loss",
# Reachability
"Silent Loss", "Passive Dropout", "Active Dropout",
# Clinical Outcomes
"Mortality (Death)", "Severe Morbidity",
# Barriers
"Geographic Attrition", "Economic Attrition", "Social Stigma",
# System Failures
"Administrative Loss", "Provider Relocation", "Protocol Burden",
# Timelines
"Intermittent Loss", "Permanent Loss"
]
}
def evaluate_patient_state(self, patient_text: str) -> Dict[str, Any]:
"""
Patient chat pannumbothu intha function than antha Text-a read panni,
mela irukkura antha categories-la etha match aaguthu nu theliva analyze pannum.
"""
# ==========================================
# REAL AI LOGIC WILL BE IMPLEMENTED HERE
# (Tokenizing, running through BERT, extracting exact category)
# ==========================================
# MOCK RETURN FOR NOW based on rule definitions
return {
"mapped_condition": "Smiling Depression (High-Functioning)",
"mapped_treatment_state": "Incomplete Relief",
"attrition_risk": "High (Possible Passive Dropout)",
"requires_doctor": False,
"extracted_feeling": "Hiding deep sadness while working normally."
}
# Initialize a global instance to be used by the FastAPI router
analyzer_engine = ClinicalAssessmentEngine()